



"""simple docstring"""






import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_=1	)						->				List[str]:
  if n_shave_prefix_segments >= 0:
    return ".".join(path.split('''.'''	)[n_shave_prefix_segments:]	)
  else:
    return ".".join(path.split('''.'''	)[:n_shave_prefix_segments]	)




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_=0	)						->				Tuple:
  _snake_case   	=      []
  for old_item in old_list:
    _snake_case   	=      old_item.replace('''in_layers.0'''  ,						'''norm1'''	)
    _snake_case   	=      new_item.replace('''in_layers.2'''  ,						'''conv1'''	)

    _snake_case   	=      new_item.replace('''out_layers.0'''  ,						'''norm2'''	)
    _snake_case   	=      new_item.replace('''out_layers.3'''  ,						'''conv2'''	)

    _snake_case   	=      new_item.replace('''emb_layers.1'''  ,						'''time_emb_proj'''	)
    _snake_case   	=      new_item.replace('''skip_connection'''  ,						'''conv_shortcut'''	)

    _snake_case   	=      shave_segments(lowerCAmelCase_  ,						n_shave_prefix_segments=lowerCAmelCase_	)

    mapping.append({'''old''': old_item, '''new''': new_item}	)

  return mapping




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_=0	)						->				Dict:
  _snake_case   	=      []
  for old_item in old_list:
    _snake_case   	=      old_item

    _snake_case   	=      new_item.replace('''norm.weight'''  ,						'''group_norm.weight'''	)
    _snake_case   	=      new_item.replace('''norm.bias'''  ,						'''group_norm.bias'''	)

    _snake_case   	=      new_item.replace('''proj_out.weight'''  ,						'''proj_attn.weight'''	)
    _snake_case   	=      new_item.replace('''proj_out.bias'''  ,						'''proj_attn.bias'''	)

    _snake_case   	=      shave_segments(lowerCAmelCase_  ,						n_shave_prefix_segments=lowerCAmelCase_	)

    mapping.append({'''old''': old_item, '''new''': new_item}	)

  return mapping




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None	)						->				List[Any]:
  assert isinstance(lowerCAmelCase_  ,						lowerCAmelCase_	), "Paths should be a list of dicts containing 'old' and 'new' keys."

  # Splits the attention layers into three variables.
  if attention_paths_to_split is not None:
    for path, path_map in attention_paths_to_split.items():
      _snake_case   	=      old_checkpoint[path]
      _snake_case   	=      old_tensor.shape[0] // 3

      _snake_case   	=      (-1, channels) if len(old_tensor.shape	) == 3 else (-1)

      _snake_case   	=      old_tensor.shape[0] // config['''num_head_channels'''] // 3

      _snake_case   	=      old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]	)
      _snake_case	,     _snake_case	,     _snake_case   	=      old_tensor.split(channels // num_heads  ,						dim=1	)

      _snake_case   	=      query.reshape(lowerCAmelCase_	)
      _snake_case   	=      key.reshape(lowerCAmelCase_	)
      _snake_case   	=      value.reshape(lowerCAmelCase_	)

  for path in paths:
    _snake_case   	=      path['''new''']

    # These have already been assigned
    if attention_paths_to_split is not None and new_path in attention_paths_to_split:
      continue

    # Global renaming happens here
    _snake_case   	=      new_path.replace('''middle_block.0'''  ,						'''mid_block.resnets.0'''	)
    _snake_case   	=      new_path.replace('''middle_block.1'''  ,						'''mid_block.attentions.0'''	)
    _snake_case   	=      new_path.replace('''middle_block.2'''  ,						'''mid_block.resnets.1'''	)

    if additional_replacements is not None:
      for replacement in additional_replacements:
        _snake_case   	=      new_path.replace(replacement['''old''']  ,						replacement['''new''']	)

        # proj_attn.weight has to be converted from conv 1D to linear
    if "proj_attn.weight" in new_path:
      _snake_case   	=      old_checkpoint[path['''old''']][:, :, 0]
    else:
      _snake_case   	=      old_checkpoint[path['''old''']]




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_	)						->				Dict:
  _snake_case   	=      {}

  _snake_case   	=      checkpoint['''time_embed.0.weight''']
  _snake_case   	=      checkpoint['''time_embed.0.bias''']
  _snake_case   	=      checkpoint['''time_embed.2.weight''']
  _snake_case   	=      checkpoint['''time_embed.2.bias''']

  _snake_case   	=      checkpoint['''input_blocks.0.0.weight''']
  _snake_case   	=      checkpoint['''input_blocks.0.0.bias''']

  _snake_case   	=      checkpoint['''out.0.weight''']
  _snake_case   	=      checkpoint['''out.0.bias''']
  _snake_case   	=      checkpoint['''out.2.weight''']
  _snake_case   	=      checkpoint['''out.2.bias''']

  # Retrieves the keys for the input blocks only
  _snake_case   	=      len({'''.'''.join(layer.split('''.'''	)[:2]	) for layer in checkpoint if '''input_blocks''' in layer}	)
  _snake_case   	=      {
      layer_id: [key for key in checkpoint if f"""input_blocks.{layer_id}""" in key]
      for layer_id in range(lowerCAmelCase_	)
  }

  # Retrieves the keys for the middle blocks only
  _snake_case   	=      len({'''.'''.join(layer.split('''.'''	)[:2]	) for layer in checkpoint if '''middle_block''' in layer}	)
  _snake_case   	=      {
      layer_id: [key for key in checkpoint if f"""middle_block.{layer_id}""" in key]
      for layer_id in range(lowerCAmelCase_	)
  }

  # Retrieves the keys for the output blocks only
  _snake_case   	=      len({'''.'''.join(layer.split('''.'''	)[:2]	) for layer in checkpoint if '''output_blocks''' in layer}	)
  _snake_case   	=      {
      layer_id: [key for key in checkpoint if f"""output_blocks.{layer_id}""" in key]
      for layer_id in range(lowerCAmelCase_	)
  }

  for i in range(1  ,						lowerCAmelCase_	):
    _snake_case   	=      (i - 1) // (config['''num_res_blocks'''] + 1)
    _snake_case   	=      (i - 1) % (config['''num_res_blocks'''] + 1)

    _snake_case   	=      [key for key in input_blocks[i] if f"""input_blocks.{i}.0""" in key]
    _snake_case   	=      [key for key in input_blocks[i] if f"""input_blocks.{i}.1""" in key]

    if f"""input_blocks.{i}.0.op.weight""" in checkpoint:
      _snake_case   	=      checkpoint[
          f"""input_blocks.{i}.0.op.weight"""
      ]
      _snake_case   	=      checkpoint[
          f"""input_blocks.{i}.0.op.bias"""
      ]
      continue

    _snake_case   	=      renew_resnet_paths(lowerCAmelCase_	)
    _snake_case   	=      {'''old''': f"""input_blocks.{i}.0""", '''new''': f"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
    _snake_case   	=      {'''old''': '''resnets.2.op''', '''new''': '''downsamplers.0.op'''}
    assign_to_checkpoint(
        lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						additional_replacements=[meta_path, resnet_op]  ,						config=lowerCAmelCase_	)

    if len(lowerCAmelCase_	):
      _snake_case   	=      renew_attention_paths(lowerCAmelCase_	)
      _snake_case   	=      {
          '''old''': f"""input_blocks.{i}.1""",
          '''new''': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
      }
      _snake_case   	=      {
          f"""input_blocks.{i}.1.qkv.bias""": {
              '''key''': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
              '''query''': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
              '''value''': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
          },
          f"""input_blocks.{i}.1.qkv.weight""": {
              '''key''': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
              '''query''': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
              '''value''': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
          },
      }
      assign_to_checkpoint(
          lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						additional_replacements=[meta_path]  ,						attention_paths_to_split=lowerCAmelCase_  ,						config=lowerCAmelCase_  ,						)

  _snake_case   	=      middle_blocks[0]
  _snake_case   	=      middle_blocks[1]
  _snake_case   	=      middle_blocks[2]

  _snake_case   	=      renew_resnet_paths(lowerCAmelCase_	)
  assign_to_checkpoint(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						config=lowerCAmelCase_	)

  _snake_case   	=      renew_resnet_paths(lowerCAmelCase_	)
  assign_to_checkpoint(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						config=lowerCAmelCase_	)

  _snake_case   	=      renew_attention_paths(lowerCAmelCase_	)
  _snake_case   	=      {
      '''middle_block.1.qkv.bias''': {
          '''key''': '''mid_block.attentions.0.key.bias''',
          '''query''': '''mid_block.attentions.0.query.bias''',
          '''value''': '''mid_block.attentions.0.value.bias''',
      },
      '''middle_block.1.qkv.weight''': {
          '''key''': '''mid_block.attentions.0.key.weight''',
          '''query''': '''mid_block.attentions.0.query.weight''',
          '''value''': '''mid_block.attentions.0.value.weight''',
      },
  }
  assign_to_checkpoint(
      lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						attention_paths_to_split=lowerCAmelCase_  ,						config=lowerCAmelCase_	)

  for i in range(lowerCAmelCase_	):
    _snake_case   	=      i // (config['''num_res_blocks'''] + 1)
    _snake_case   	=      i % (config['''num_res_blocks'''] + 1)
    _snake_case   	=      [shave_segments(lowerCAmelCase_  ,						2	) for name in output_blocks[i]]
    _snake_case   	=      {}

    for layer in output_block_layers:
      _snake_case	,     _snake_case   	=      layer.split('''.'''	)[0], shave_segments(lowerCAmelCase_  ,						1	)
      if layer_id in output_block_list:
        output_block_list[layer_id].append(lowerCAmelCase_	)
      else:
        _snake_case   	=      [layer_name]

    if len(lowerCAmelCase_	) > 1:
      _snake_case   	=      [key for key in output_blocks[i] if f"""output_blocks.{i}.0""" in key]
      _snake_case   	=      [key for key in output_blocks[i] if f"""output_blocks.{i}.1""" in key]

      _snake_case   	=      renew_resnet_paths(lowerCAmelCase_	)
      _snake_case   	=      renew_resnet_paths(lowerCAmelCase_	)

      _snake_case   	=      {'''old''': f"""output_blocks.{i}.0""", '''new''': f"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
      assign_to_checkpoint(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						additional_replacements=[meta_path]  ,						config=lowerCAmelCase_	)

      if ["conv.weight", "conv.bias"] in output_block_list.values():
        _snake_case   	=      list(output_block_list.values()	).index(['''conv.weight''', '''conv.bias''']	)
        _snake_case   	=      checkpoint[
            f"""output_blocks.{i}.{index}.conv.weight"""
        ]
        _snake_case   	=      checkpoint[
            f"""output_blocks.{i}.{index}.conv.bias"""
        ]

        # Clear attentions as they have been attributed above.
        if len(lowerCAmelCase_	) == 2:
          _snake_case   	=      []

      if len(lowerCAmelCase_	):
        _snake_case   	=      renew_attention_paths(lowerCAmelCase_	)
        _snake_case   	=      {
            '''old''': f"""output_blocks.{i}.1""",
            '''new''': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
        }
        _snake_case   	=      {
            f"""output_blocks.{i}.1.qkv.bias""": {
                '''key''': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
                '''query''': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
                '''value''': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
            },
            f"""output_blocks.{i}.1.qkv.weight""": {
                '''key''': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
                '''query''': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
                '''value''': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
            },
        }
        assign_to_checkpoint(
            lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						additional_replacements=[meta_path]  ,						attention_paths_to_split=to_split if any('''qkv''' in key for key in attentions	) else None  ,						config=lowerCAmelCase_  ,						)
    else:
      _snake_case   	=      renew_resnet_paths(lowerCAmelCase_  ,						n_shave_prefix_segments=1	)
      for path in resnet_0_paths:
        _snake_case   	=      '''.'''.join(['''output_blocks''', str(lowerCAmelCase_	), path['''old''']]	)
        _snake_case   	=      '''.'''.join(['''up_blocks''', str(lowerCAmelCase_	), '''resnets''', str(lowerCAmelCase_	), path['''new''']]	)

        _snake_case   	=      checkpoint[old_path]

  return new_checkpoint


if __name__ == "__main__":
     snake_case   					=				argparse.ArgumentParser()

     parser.add_argument(
         '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
     )

     parser.add_argument(
         '''--config_file''',
         default=None,
         type=str,
         required=True,
         help='''The config json file corresponding to the architecture.''',
     )

     parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

     snake_case   					=				parser.parse_args()

     snake_case   					=				torch.load(args.checkpoint_path)

     with open(args.config_file) as f:
          snake_case   					=				json.loads(f.read())

     snake_case   					=				convert_ldm_checkpoint(checkpoint, config)

     if "ldm" in config:
          del config["ldm"]

     snake_case   					=				UNetaDModel(**config)
     model.load_state_dict(converted_checkpoint)

     try:
          snake_case   					=				DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
          snake_case   					=				VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

          snake_case   					=				LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
          pipe.save_pretrained(args.dump_path)
     except:  # noqa: E722
          model.save_pretrained(args.dump_path)










'''simple docstring'''

# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)

@flax.struct.dataclass
class 							a__:
			a_	: CommonSchedulerState

			# setable values
			a_	: jnp.ndarray
			a_	: jnp.ndarray
			a_	: Optional[int]									=      None






			@classmethod
			def 						_lowercase  (     cls					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							Dict:
					return cls(common=_UpperCAmelCase					,   init_noise_sigma=_UpperCAmelCase					,   timesteps=_UpperCAmelCase						)





@dataclass
class 							a__( snake_case__     ):
			a_	: DDPMSchedulerState





class 							a__( snake_case__   ,      snake_case__     ):
			a_	: Union[str, Any]									=      [e.name for e in FlaxKarrasDiffusionSchedulers]

			a_	: jnp.dtype






			@property
			def 						_lowercase  (     self						)							->							Union[str, Any]:
					return True






			@register_to_config
			def __init__(     self					,   _UpperCAmelCase = 1000					,   _UpperCAmelCase = 0.0_001					,   _UpperCAmelCase = 0.02					,   _UpperCAmelCase = "linear"					,   _UpperCAmelCase = None					,   _UpperCAmelCase = "fixed_small"					,   _UpperCAmelCase = True					,   _UpperCAmelCase = "epsilon"					,   _UpperCAmelCase = jnp.floataa					,   )							->							Union[str, Any]:
					snake_case__	     =dtype






			def 						_lowercase  (     self					,   _UpperCAmelCase = None						)							->							DDPMSchedulerState:
					if common is None:
							snake_case__	     =CommonSchedulerState.create(self						)

					# standard deviation of the initial noise distribution
					snake_case__	     =jnp.array(1.0					,   dtype=self.dtype						)

					snake_case__	     =jnp.arange(0					,   self.config.num_train_timesteps						).round()[::-1]

					return DDPMSchedulerState.create(
					    common=_UpperCAmelCase					,   init_noise_sigma=_UpperCAmelCase					,   timesteps=_UpperCAmelCase					,   )






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase = None						)							->							jnp.ndarray:
					return sample






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase = ()						)							->							DDPMSchedulerState:
					snake_case__	     =self.config.num_train_timesteps // num_inference_steps
					# creates integer timesteps by multiplying by ratio
					# rounding to avoid issues when num_inference_step is power of 3
					snake_case__	     =(jnp.arange(0					,   _UpperCAmelCase						) * step_ratio).round()[::-1]

					return state.replace(
					    num_inference_steps=_UpperCAmelCase					,   timesteps=_UpperCAmelCase					,   )






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase=None					,   _UpperCAmelCase=None						)							->							Optional[Any]:
					snake_case__	     =state.common.alphas_cumprod[t]
					snake_case__	     =jnp.where(t > 0					,   state.common.alphas_cumprod[t - 1]					,   jnp.array(1.0					,   dtype=self.dtype						)						)

					# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
					# and sample from it to get previous sample
					# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
					snake_case__	     =(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

					if variance_type is None:
							snake_case__	     =self.config.variance_type

					# hacks - were probably added for training stability
					if variance_type == "fixed_small":
							snake_case__	     =jnp.clip(_UpperCAmelCase					,   a_min=1E-20						)
					# for rl-diffuser https://arxiv.org/abs/2205.09991
					elif variance_type == "fixed_small_log":
							snake_case__	     =jnp.log(jnp.clip(_UpperCAmelCase					,   a_min=1E-20						)						)
					elif variance_type == "fixed_large":
							snake_case__	     =state.common.betas[t]
					elif variance_type == "fixed_large_log":
							# Glide max_log
							snake_case__	     =jnp.log(state.common.betas[t]						)
					elif variance_type == "learned":
							return predicted_variance
					elif variance_type == "learned_range":
							snake_case__	     =variance
							snake_case__	     =state.common.betas[t]
							snake_case__	     =(predicted_variance + 1) / 2
							snake_case__	     =frac * max_log + (1 - frac) * min_log

					return variance






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase = None					,   _UpperCAmelCase = True					,   )							->							Union[FlaxDDPMSchedulerOutput, Tuple]:
					snake_case__	     =timestep

					if key is None:
							snake_case__	     =jax.random.PRNGKey(0						)

					if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
							snake_case__     ,	snake_case__	     =jnp.split(_UpperCAmelCase					,   sample.shape[1]					,   axis=1						)
					else:
							snake_case__	     =None

					# 1. compute alphas, betas
					snake_case__	     =state.common.alphas_cumprod[t]
					snake_case__	     =jnp.where(t > 0					,   state.common.alphas_cumprod[t - 1]					,   jnp.array(1.0					,   dtype=self.dtype						)						)
					snake_case__	     =1 - alpha_prod_t
					snake_case__	     =1 - alpha_prod_t_prev

					# 2. compute predicted original sample from predicted noise also called
					# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
					if self.config.prediction_type == "epsilon":
							snake_case__	     =(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
					elif self.config.prediction_type == "sample":
							snake_case__	     =model_output
					elif self.config.prediction_type == "v_prediction":
							snake_case__	     =(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
					else:
							raise ValueError(
							    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
							    ' for the FlaxDDPMScheduler.'						)

					# 3. Clip "predicted x_0"
					if self.config.clip_sample:
							snake_case__	     =jnp.clip(_UpperCAmelCase					,   -1					,   1						)

					# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
					# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
					snake_case__	     =(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
					snake_case__	     =state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

					# 5. Compute predicted previous sample µ_t
					# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
					snake_case__	     =pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

					# 6. Add noise
					def random_variance():
							snake_case__	     =jax.random.split(_UpperCAmelCase					,   num=1						)
							snake_case__	     =jax.random.normal(_UpperCAmelCase					,   shape=model_output.shape					,   dtype=self.dtype						)
							return (self._get_variance(_UpperCAmelCase					,   _UpperCAmelCase					,   predicted_variance=_UpperCAmelCase						) ** 0.5) * noise

					snake_case__	     =jnp.where(t > 0					,   random_variance()					,   jnp.zeros(model_output.shape					,   dtype=self.dtype						)						)

					snake_case__	     =pred_prev_sample + variance

					if not return_dict:
							return (pred_prev_sample, state)

					return FlaxDDPMSchedulerOutput(prev_sample=_UpperCAmelCase					,   state=_UpperCAmelCase						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   )							->							jnp.ndarray:
					return add_noise_common(state.common					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   )							->							jnp.ndarray:
					return get_velocity_common(state.common					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)






			def __len__(     self						)							->							Optional[int]:
					return self.config.num_train_timesteps




